15.1 Populations, Guilds, and Communities 15.2 Environments and Microenvironments 环境与微环境 15.3 Microbial Growth on Surfaces and Biofilms 表面生长与生物膜 15.4 Terrestrial Environments 15.5 Freshwater habitats 淡水生境 15.6. Marine Microbiology

一、数量性状 二、数量性状的遗传机制 三、研究数量性状的几个遗传参数 四、近交和杂交 五、数量性状分析的分子方法

14.1 Enrichment and Isolation 14.2 Isolation 14.3 General Staining Methods 14.4 Phylogenetic staining using FISH 系统发育学染色: FISH技术 14.5 PCR Methods of Microbial Community Analysis 14.6 Microarrays and Microbial Diversity: Phylochips 系统发育学微阵列（芯片） 14.7 Environmental Genomics and Related Methods

13.15 Fermentations: Energetic and redox consideration 13.16 Fermentative Diversity 13.17 Fermentations lacking substrate level phosphorylation 13.18 Anaerobic respiration: : General Principles 13.19 Nitrate reduction and Denitrification硝酸还原与反硝化 13.20 Sulfate Reduction 硫还原 13.21 Methanogenesis 产甲烷过程 13.22 Other Electron Acceptors

13.6 The Energetics of Chemolithotrophy 13.7 Hydrogen Oxidation 氢的氧化 13.8 Oxidation of reduced sulfur compounds 还原态硫的氧化 13.9 Iron Oxidation 铁氧化 13.10 Nitrifying bacteria 硝化细菌 13.11 Anammox Major Biosyntheses: Autotrophy and Nitrogen Fixation 13.12 Autotrophic CO2 fixation: The Calvin cycle卡尔文循环固定CO2 13.13 Autotrophic CO2 fixation: Other autotrophic pathways in phototrophs 13.14 Nitrogenase and nitrogen fixation 固氮酶与固氮作用

13.1 Photosynthesis 13.2 Chlorophylls and Bacteriochlorophylls 叶绿素与菌绿素 13.3 Accessory pigments 其它色素 13.4 Anoxygenic Photosynthesis: purple bacteria as example 不产氧光合作用 13.5 Oxygenic photosythesis 产氧光合作用

1 introduction 单变量时间序列模型 一只利用变量的过去信息和可能的误差项的当前和过去值来建模和预测 的一类模型(设定)

一、目的基因( gene of irreresi)的获取 二、目的基因与载体结合成重组DIA 三、重组DNA分子引入受体细胞并表达 四、转化体细胞的扩增、鉴定与筛选

1 Stationarity and unit Root testing WHy do we need to test for Non-Stationarity? The stationarity or otherwise of a series can strongly influence its behaviour and properties -e.g. persistence of shocks will be infinite for nonstationary series Spurious regressions. If two variables are trending over

1 Goodness of Fit Statistics We would like some measure of how well our regression model actually fits the data.* We have goodness of fit statistics to test this: i.e. how well the sample regression function (srf) fits the data. The most common goodness of fit statistic is known as R2. One